scholarly journals Creating a 2D Active Shape Model Using itk::ImagePCAShapeModelEstimator

2011 ◽  
Author(s):  
John Durkin ◽  
David Miller ◽  
Kenneth Urish
Keyword(s):  
Active Contour ◽  
Active Shape Model ◽  
Knee Cartilage ◽  
Shape Model ◽  
Active Shape Models ◽  
Contour Segmentation ◽  
Shape Models ◽  
Segmentation Algorithms ◽  
Active Shape ◽  
Weak Boundaries

Although many variations of active contour segmentation algorithms exist, most are based on solely edge criteria and breakdown or leak at weak boundaries. One solution to this problem is constraining the segmented area to only statistically possible shapes with the guidance of a shape model. The purpose of this document is to fill the void in the ITK user guide on building active shape models. We describe how to create a 2d active shape model of articular femoral knee cartilage using ITK’s ImagePCAShapeModelEstimator. Sample code and example images are provided for displaying the initial principle components of variation. Shape models built with our code can be used for segmentation with itk::GeodesicActiveContourShapePriorLevelSetImageFilter.

scholarly journals Intensity Profiles in Active Shape Model

2020 ◽  
Vol 34 (5) ◽  
pp. 531-539
Author(s):  
Moulkheir Naoui ◽  
Ghalem Belalem
Keyword(s):  
Image Segmentation ◽  
Active Shape Model ◽  
Complex Structures ◽  
Shape Model ◽  
Object Shape ◽  
Active Shape Models ◽  
Matching Process ◽  
Shape Models ◽  
Active Shape ◽  
The Right

Active shape model is a deformable model which has proven very successful results in the field of image segmentation. The success of ASM model lies in its ability to find the right positions of all landmark points which define the object shape. Intensity profiles are an important part of the Active Shape Models (ASM) which help steer and optimize matching process. However, their simplicity in the standard version of the ASM turns into weakness. The difficulties are met when they are applied to complex structures. The main purpose of this paper is to give a review and discussion about the alternatives proposed in the literature that provide more elaborated intensity models and their impact on the performance of ASM.

scholarly journals KLASIFIKASI OSTEOARTHRITIS BERBASIS DUAL TREE COMPLEX WAVELET TRANSFORM DAN CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION (CLAHE) MENGGUNAKAN ACTIVE SHAPE MODELS

2020 ◽  
Vol 3 (1) ◽  
pp. 15
Author(s):  
Mohammad Meizaki Fatihin ◽  
Farid Baskoro ◽  
Arif Widodo
Keyword(s):  
Histogram Equalization ◽  
Complex Wavelet Transform ◽  
Shape Model ◽  
X Ray ◽  
Active Shape Models ◽  
Adaptive Histogram Equalization ◽  
Shape Models ◽  
Active Shape ◽  
Osteoarthritis Initiative ◽  
Grade 3

Citra adalah representasi dari informasi yang terkandung di dalamnya sehingga mata manusia dapat menganalisis dan menafsirkan informasi sesuai dengan tujuan yang diharapkan. Salah satu bentuk citra medis adalah citra x-ray. Penelitian ini mengidentifikasi gambar x-ray Osteoarthritis Lutut yang diambil pada berbagai tingkat keparahan, mulai dari KL-Grade 0 hingga KL-Grade 4. Penelitian ini menggunakan metode CLAHE dan DTCWT untuk proses preprosessing dan menggunakan metode Active Shape Model (ASM) untuk proses segmentasi, menggunakan 35 data pelatihan dan 200 data uji dari Osteoarthritis Initiative (OAI). Pengujian citra uji dalam penelitian ini dengan mengekstraksi tekstur citra menggunakan metode GLCM dan segmentasi citra menggunakan ASM, sehingga proses scanning untuk penentuan titik-titik yang berfungsi untuk mengukur ketebalan cartilage. Hasil Ekstraksi tekstur memiliki tingkat akurasi klasifikasi KL-Grade 0 57,5%, KL-Grade 1 memiliki akurasi 33.3%, KL-Grade 2 37,5%, KL-Grade 3 37,5% dan KL-Grade 4 34,3 %. Sedangkan untuk pengukuran ketebalan tulang rawan memiliki akurasi klasifikasi untuk KL-Grade 0 sebesar 62.5%, kemudian KL-Grade 1 sebesar 44.4 %, sedangkan untuk KL-Grade 2 memiliki keberhasilan klasifikasi 60%, kemudian KL-Grade 3 memiliki klasifikasi berhasil dengan benar 70%, dan untuk KL-Grade 4 51.4%.

Traffic Congestion Pattern Classification Using Multiclass Active Shape Models

2017 ◽  
Vol 2645 (1) ◽  
pp. 94-103 ◽  
Author(s):  
Panchamy Krishnakumari ◽  
Tin Nguyen ◽  
Léonie Heydenrijk-Ottens ◽  
Hai L. Vu ◽  
Hans van Lint
Keyword(s):  
Expert Knowledge ◽  
Statistical Shape Model ◽  
Transport Systems ◽  
Active Shape Model ◽  
Traffic Patterns ◽  
Shape Model ◽  
Active Shape Models ◽  
Contour Maps ◽  
Active Shape

Identifying and classifying traffic and congestion patterns are essential parts of modern traffic management underpinned by the emerging intelligent transport systems. This paper explores the potential of using a combination of image processing methods to identify and classify regions of congestion within spatiotemporal traffic (speed, flow) contour maps. The underlying idea is to use these regions as (archetype) shapes that in many combinations can make up a wide variety of larger-scale traffic patterns. In this paper, use of a so-called statistical shape model is proposed as a low-dimensional representation of the archetype shape, and an active shape model algorithm coupled with linear classification is developed to classify the patterns of interest. Application of the proposed method is demonstrated with a preliminary set of speed contour maps reconstructed from loop detector data in the Netherlands. The results show that the extended active shape model can be used as a multiclass classifier. In particular, 70% of the traffic patterns in the test data were correctly classified with use of only two archetype shapes and simple logistic classifiers. The results point to the importance of use of expert knowledge by means of (a priori) manual classification of the training examples. This work opens many research directions, including semiautomated searches through traffic databases, automatic detection, and classification of new traffic patterns.

Human face shape classification method based on active shape model

2009 ◽  
Vol 29 (10) ◽  
pp. 2710-2712 ◽  
Author(s):  
Li-qiang DU ◽  
Peng JIA ◽  
Zong-tan ZHOU ◽  
De-wen HU
Keyword(s):  
Classification Method ◽  
Active Shape Model ◽  
Shape Classification ◽  
Human Face ◽  
Shape Model ◽  
Face Shape ◽  
Active Shape

scholarly journals Deformable Models for Segmentation Based on Local Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jimena Olveres ◽  
Erik Carbajal-Degante ◽  
Boris Escalante-Ramírez ◽  
Enrique Vallejo ◽  
Carla María García-Moreno
Keyword(s):  
Local Binary Patterns ◽  
Distance Measures ◽  
Local Analysis ◽  
Hermite Transform ◽  
Active Shape Models ◽  
Shape Models ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Image Modality ◽  
New Algorithms

Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

Echocardiogram segmentation using active shape model and mean squared eigenvalue error

2021 ◽  
Vol 69 ◽  
pp. 102807
Author(s):  
Yasser Ali ◽  
Soosan Beheshti ◽  
Farrokh Janabi-Sharifi
Keyword(s):  
Active Shape Model ◽  
Shape Model ◽  
Active Shape
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